Director of Software Engineering - Big Data and Cloud Platforms

Job Description

Director of Software Engineering - Big Data, Cloud We have an opportunity to impact your career and provide an adventure where you can push the limits of whats possible. As a Director of Software Engineering at JPMorgan Chase within Corporate Technology, you lead a technical area and drive impact within teams, technologies, and projects across departments. Utilize your in-depth knowledge of software, applications, technical processes, and product management to drive multiple complex projects and initiatives, while serving as a primary decision maker for your teams and be a driver of innovation and solution delivery. Job responsibilities Leads the delivery of Trade Surveillance solutions aligned with business goals and regulatory requirements Drives the platform modernization initiative, leveraging Data Mesh principles on Databricks to build scalable, resilient, and efficient big data platforms Provides hands-on technical leadership and guidance to engineering teams, ensuring engineering excellence, secure-by-design, and agile/DevOps best practices Manages multiple feature teams, acting as both a people supervisor and technical leader; hire, develop, mentor, and recognise talent across cross-functional teams Owns decisions on team resources, budgets, and tactical operations, including process execution and continuous optimisation. Evaluates, selects, and integrates emerging technologiesincluding AI/ML solutionsto enhance surveillance coverage, accuracy, efficiency and also leads and drives AI projects, fostering AI readiness and adoption with the global team. Builds and nurture a collaborative environment that encourages innovation and continuous improvement. Identifies and mitigate risks across data, models, security, and operations; ensure audit readiness and control compliance. Develops strong partnerships with product, compliance, and LOB Technology also drives transformation and modernisation projects to achieve shared objectives. Sets direction and governance for agentic AI-enabled engineering and SDLC/TLM automation within a technical area to drive measurable improvements in speed, quality, and operational outcomes (e.g., AI-orchestrated delivery workflows, release readiness controls, automated test modernisation, and incident triage acceleration), while establishing guardrails for validation, security, resiliency, traceability, and reuse across teams Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realised by automation and support capacity unlock initiatives at scale Required qualifications, capabilities, and skills Solid track record and 10 years of experience in building large-scale data warehouses, enterprise data platforms, and critical big data solutions Proven experience in cloud implementation and practical cloud-native development with AWS, Azure, or GCP Deep expertise in platform building, modernization, and transformation projects, including Data Mesh and Databricks Familiarity with AI/ML technologies and experience leading or driving AI projects Experience developing or leading cross-functional teams of technologists, including hiring, developing, and recognising talent Strong people management skills; ability to supervise, mentor, and grow engineering leaders and teams Excellent communication, collaboration, and stakeholder management skills Ability to drive innovation and foster a culture of continuous improvement Experience managing multiple Scrum/feature teams and delivering complex projects in an agile environment Experience leading adoption of agentic AI-enabled engineering practices (using enterprise-authorised tools within the work environment) across teams, including defining operating expectations (human-in-the-loop validation, quality gates), measuring outcomes, and ensuring secure handling of sensitive inputs/outputs Strong understanding of responsible AI use and control expectations in engineering workflows, including data sensitivity, resiliency/security implications, and governance; ability to influence leaders on safe scaling patterns and reuse Preferred qualifications, capabilities, and skills Experience working at code level Experience Level Executive Level .

Keyskills :
Software EngineeringBig DataAWSAzureGCPPeople ManagementCloud PlatformsData MeshDatabricks